Video identification and analytical recognition system

ABSTRACT

An analytical recognition system includes one or more video cameras configured to capture video and a video analytics module configured to perform real-time video processing and analyzation of the captured video and generate non-video data. The video analytic module includes one or more algorithms configured to identify an abnormal situation. Each abnormal situation alerts the video analytics module to automatically issue an alert and track one or more objects or individuals by utilizing the one or more video cameras. The abnormal situation is selected from the group consisting of action of a particular individual, non-action of a particular individual, a temporal event, and an externally generated event.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims priority to, and the benefit of, U.S.Provisional Application Ser. No. 61/813,942, filed on Apr. 19, 2013, thedisclosure of which is herein incorporated by reference in its entirety.

BACKGROUND

1. Technical Field

The following relates to video observation, surveillance andverification systems and methods of use. The specific application maywork in conjunction with surveillance systems, street cameras, personalvideo, in-store camera systems, parking lot camera systems, etc. and isconfigured to provide real time and/or post time data analysis of one ormore video streams.

2. Background of Related Art

Companies are continually trying to identify specific user behavior inorder to improve the throughput and efficiency of the company. Forexample, by understanding user behavior in the context of the retailindustry, companies can both improve product sales and reduce productshrinkage. Focusing on the latter, employee theft is one of the largestcomponents of retail inventory shrink. Therefore, companies are tryingto understand user behavior in order to reduce and ultimately eliminateinventory shrinkage.

Companies have utilized various methods to prevent employee shrinkage.Passive electronic devices attached to theft-prone items in retailstores are used to trigger alarms, although customers and/or employeesmay deactivate these devices before an item leaves the store. Someretailers conduct bag and/or cart inspections for both customers andemployees while other retailers have implemented loss prevention systemsthat incorporate video monitoring of POS transactions to identifytransactions that may have been conducted in violation of implementedprocedures. Most procedures and technologies focus on identifyingindividual occurrences instead of understanding the underlying userbehaviors that occur during these events. As such, companies are unableto address the underlying condition that allows individuals to committheft.

Surveillance systems, street camera systems, store camera systems,parking lot camera systems, and the like are widely used. In certaininstances, camera video is continually streaming and a buffer period of8, 12, 24, 48 hours, for example, is used and then overwritten should aneed not arise for the video. In other systems, a longer period of timemay be utilized or the buffer is weeks or months of data being storedand saved for particular purposes. As can be appreciated, when an eventoccurs, the video is available for review and analysis of the videodata. In some instances, the video stream captures data and analyzesvarious pre-determined scenarios based upon automatic, user input, orprogramming depending upon a particular purpose. For example, the videomay be programmed to follow moving objects from entry into a store andthroughout the store for inventory control and/or video monitoring ofcustomers.

In other instances, police, FBI or rescue personal need to review thevarious camera systems in a particular area or arena for investigativepurposes, e.g., to track suspects, for car accident review, or othervideo evidence necessary to their investigation. As is often the case,snippets of video from various camera systems throughout the area can becritical in piecing together a visual map of the event in question. Inother scenarios, an individual's habits or behaviors may becomesuspicious and deserved of monitoring or tracking for real-time analysisand alerts and/or post time investigative analysis.

There exists a need to further develop this analytical technology andprovide real time and post time analysis of video streams for securityand investigative purposes.

SUMMARY

According to an aspect of the present disclosure, an analyticalrecognition system is provided. The analytical recognition systemincludes one or more video cameras configured to capture video and avideo analytics module configured to perform real-time video processingand analyzation of the captured video and generate non-video data. Thevideo analytic module includes one or more algorithms configured toidentify an abnormal situation. Each abnormal situation alerts the videoanalytics module to automatically issue an alert and track one or moreobjects or individuals by utilizing the one or more video cameras. Theabnormal situation is selected from the group consisting of action of aparticular individual, non-action of a particular individual, a temporalevent, and an externally generated event.

In any one of the preceding aspects, the video analytics moduleidentifies and stores in a database one or more characteristics of theparticular individual for future recognition by the video analyticsmodule and the one or more algorithms to identify an abnormal situation.The one or more characteristics of the particular individual may beselected from the group consisting of hairstyle, tattoos, piercings,clothing, logos, contrasting colors, gang-related indicia, and jewelry.

In any one of the preceding aspects, the video analytics module storesthe captured video in a database accessible by a user and wherein theuser identifies one or more characteristics of the particular individualfor future recognition by the video analytics module and the one or morealgorithms to identify an abnormal situation.

In any one of the preceding aspects, the video analytics moduleidentifies and stores in a database one or more characteristics of theparticular individual for future recognition by the video analyticsmodule and the one or more algorithms to identify an abnormal situationand issue an alert wherein the video analytics module connects to anarray of cameras organized in a network to analyze captured video.

In any one of the preceding aspects, the one or more characteristics ofthe particular individual may be selected from the group consisting ofhairstyle, tattoos, piercings, clothing, logos, contrasting colors,gang-related indicia, and jewelry.

In any one of the preceding aspects, the one or more characteristics ofthe particular individual includes a person's gait. Each person's gaitmay be determined based on a combination of one or more of the followingwalking variables including: limp, shuffle, head angle, stride, hand/armsway, hand gestures, walk velocity, step frequency, angle between feet,and hand/arm position.

According to another aspect of the present disclosure, an analyticalrecognition system is provided and includes one or more video camerasconfigured to capture a video sequence of a physical space and a videoanalytics module configured to perform real-time video processing andanalyzation to determine a crowd parameter by automated processing ofthe video sequence of the physical space. The video analytic moduleincludes one or more algorithms configured to determine a rate of changein the crowd parameter.

In any one of the preceding aspects, the crowd parameter may be areal-time crowd count or a real-time crowd density estimation.

In any one of the preceding aspects, the when the rate of change in thecrowd parameter exceeds a predetermined threshold, the video analyticsmodule automatically issues an alert.

In any one of the preceding aspects, the rate of change in the crowdparameter is indicative of crowd convergence. When the rate of change inthe crowd parameter is indicative of crowd convergence, the videoanalytics module may alert security of a potential flash mob or gangrobbery.

In any one of the preceding aspects, the rate of change in the crowdparameter is indicative of crowd divergence. When the rate of change inthe crowd parameter is indicative of crowd divergence, the videoanalytics module may alert security of a potentially hazardous situationor criminal activity.

In any one of the preceding aspects, the video analytics module isconnected to an array of cameras organized in a network and wherein uponissuance of an alert each camera in the network is utilized to track oneor more objects or individuals. An owner of one of the cameras in thearray of cameras forming the network may opt on a subscription basis forreceiving particular alerts or being part of the camera network.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a system block diagram of an embodiment of a videoobservation, surveillance and verification system in accordance with thepresent disclosure;

FIG. 2 is a video/image sequencer according to an embodiment of thepresent disclosure;

FIG. 3 is an illustration of an image map and an associated timelinegenerated by the sequencer of FIG. 2;

FIG. 4 is a schematic illustration of an analytical recognition systemused for object identification and tracking according to anotherembodiment of the present disclosure;

FIG. 5 is a schematic illustration of an analytical recognition systemused for convergence tracking according to another embodiment of thepresent disclosure;

FIG. 6 is a schematic illustration of an analytical recognition systemused for character trait recognition according to another embodiment ofthe present disclosure; and

FIG. 7 is a schematic illustration of an analytical recognition systemused for a community surveillance network according to anotherembodiment of the present disclosure.

DEFINITIONS

The following definitions are applicable throughout this disclosure(including above).

A “video camera” may refer to an apparatus for visual recording.Examples of a video camera may include one or more of the following: avideo imager and lens apparatus; a video camera; a digital video camera;a color camera; a monochrome camera; a camera; a camcorder; a PC camera;a webcam; an infrared (IR) video camera; a low-light video camera; athermal video camera; a closed-circuit television (CCTV) camera; apan/tilt/zoom (PTZ) camera; and a video sensing device. A video cameramay be positioned to perform observation of an area of interest.

“Video” may refer to the motion pictures obtained from a video camerarepresented in analog and/or digital form. Examples of video mayinclude: television; a movie; an image sequence from a video camera orother observer; an image sequence from a live feed; a computer-generatedimage sequence; an image sequence from a computer graphics engine; animage sequence from a storage device, such as a computer-readablemedium, a digital video disk (DVD), or a high-definition disk (HDD); animage sequence from an IEEE 1394-based interface; an image sequence froma video digitizer; or an image sequence from a network.

“Video data” is a visual portion of the video.

“Non-video data” is non-visual information extracted from the videodata.

A “video sequence” may refer to a selected portion of the video dataand/or the non-video data.

“Video processing” may refer to any manipulation and/or analysis ofvideo data, including, for example, compression, editing, and performingan algorithm that generates non-video data from the video.

A “frame” may refer to a particular image or other discrete unit withinvideo.

A “computer” may refer to one or more apparatus and/or one or moresystems that are capable of accepting a structured input, processing thestructured input according to prescribed rules, and producing results ofthe processing as output. Examples of a computer may include: acomputer; a stationary and/or portable computer; a computer having asingle processor, multiple processors, or multi-core processors, whichmay operate in parallel and/or not in parallel; a general purposecomputer; a supercomputer; a mainframe; a super mini-computer; amini-computer; a workstation; a micro-computer; a server; a client; aninteractive television; a web appliance; a telecommunications devicewith internet access; a hybrid combination of a computer and aninteractive television; a portable computer; a tablet personal computer(PC); a personal digital assistant 123 (PDA); a portable telephone;application-specific hardware to emulate a computer and/or software,such as, for example, a digital signal processor (DSP), afield-programmable gate array (FPGA), an application specific integratedcircuit (ASIC), an application specific instruction-set processor(ASIP), a chip, chips, or a chip set; a system on a chip (SoC), or amultiprocessor system-on-chip (MPSoC); an optical computer; a quantumcomputer; a biological computer; and an apparatus that may accept data,may process data in accordance with one or more stored softwareprograms, may generate results, and typically may include input, output,storage, arithmetic, logic, and control units.

“Software” may refer to prescribed rules to operate a computer. Examplesof software may include: software; code segments; instructions; applets;pre-compiled code; compiled code; interpreted code; computer programs;and programmed logic. In this description, the terms “software” and“code” may be applicable to software, firmware, or a combination ofsoftware and firmware.

A “computer-readable medium” may refer to any storage device used forstoring data accessible by a computer. Examples of a computer-readablemedium may include: a magnetic hard disk; a floppy disk; an opticaldisk, such as a CD-ROM and a DVD; a magnetic tape; a flash removablememory; a memory chip; and/or other types of media that may storemachine-readable instructions thereon. “Non-transitory”computer-readable medium include all computer-readable medium, with thesole exception being a transitory, propagating signal.

A “computer system” may refer to a system having one or more computers,where each computer may include a computer-readable medium embodyingsoftware to operate the computer. Examples of a computer system mayinclude: a distributed computer system for processing information viacomputer systems linked by a network; two or more computer systemsconnected together via a network for transmitting and/or receivinginformation between the computer systems; and one or more apparatusesand/or one or more systems that may accept data, may process data inaccordance with one or more stored software programs, may generateresults, and typically may include input, output, storage, arithmetic,logic, and control units.

A “network” may refer to a number of computers and associated devicesthat may be connected by communication facilities. A network may involvepermanent connections such as cables or temporary connections such asthose made through telephone or other communication links. A network mayfurther include hard-wired connections (e.g., coaxial cable, twistedpair, optical fiber, waveguides, etc.) and/or wireless connections(e.g., radio frequency waveforms, free-space optical waveforms, acousticwaveforms, etc.). Examples of a network may include: an internet, suchas the Internet; an intranet; a local area network (LAN); a wide areanetwork (WAN); and a combination of networks, such as an internet and anintranet. Exemplary networks may operate with any of a number ofprotocols, such as Internet protocol (IP), asynchronous transfer mode(ATM), and/or synchronous optical network (SONET), user datagramprotocol (UDP), IEEE 802.x, etc.

“Real time” analysis or analytics generally refers to processing realtime or “live” video and providing near instantaneous reports orwarnings of abnormal conditions (pre-programmed conditions), abnormalscenarios (loitering, convergence, separation of clothing articles orbackpacks, briefcases, groceries for abnormal time, etc.) or otherscenarios based on behavior of elements (customers, patrons, people incrowd, etc.) in one or multiple video streams.

“Post time” analysis or analytics generally refers to processing storedor saved video from a camera source (from a particular camera system(e.g., store, parking lot, street) or other video data (cell phone, homemovie, etc.)) and providing reports or warnings of abnormal conditions(post-programmed conditions), abnormal scenarios (loitering,convergence, separation of clothing articles or backpacks, briefcases,groceries for abnormal time, etc. or other scenarios based on behaviorof elements (customers, patrons, people in crowd, etc.) in one or morestored video streams.

DETAILED DESCRIPTION

Particular embodiments of the present disclosure are describedhereinbelow with reference to the accompanying drawings; however, it isto be understood that the disclosed embodiments are merely examples ofthe disclosure, which may be embodied in various forms. Well-knownfunctions or constructions are not described in detail to avoidobscuring the present disclosure in unnecessary detail. Therefore,specific structural and functional details disclosed herein are not tobe interpreted as limiting, but merely as a basis for the claims and asa representative basis for teaching one skilled in the art to variouslyemploy the present disclosure in virtually any appropriately detailedstructure. In this description, as well as in the drawings,like-referenced numbers represent elements that may perform the same,similar, or equivalent functions.

Additionally, the present disclosure may be described herein in terms offunctional block components, code listings, optional selections, pagedisplays, and various processing steps. It should be appreciated thatsuch functional blocks may be realized by any number of hardware and/orsoftware components configured to perform the specified functions. Forexample, the present disclosure may employ various integrated circuitcomponents, e.g., memory elements, processing elements, logic elements,look-up tables, and the like, which may carry out a variety of functionsunder the control of one or more microprocessors or other controldevices.

Similarly, the software elements of the present disclosure may beimplemented with any programming or scripting language such as C, C++,C#, Java, COBOL, assembler, PERL, Python, PHP, or the like, with thevarious algorithms being implemented with any combination of datastructures, objects, processes, routines or other programming elements.The object code created may be executed on a variety of operatingsystems including, without limitation, Windows®, Macintosh OSX®, iOS®,linux, and/or Android®.

Further, it should be noted that the present disclosure may employ anynumber of conventional techniques for data transmission, signaling, dataprocessing, network control, and the like. It should be appreciated thatthe particular implementations shown and described herein areillustrative of the disclosure and its best mode and are not intended tootherwise limit the scope of the present disclosure in any way. Examplesare presented herein which may include sample data items (e.g., names,dates, etc.) which are intended as examples and are not to be construedas limiting. Indeed, for the sake of brevity, conventional datanetworking, application development and other functional aspects of thesystems (and components of the individual operating components of thesystems) may not be described in detail herein. Furthermore, theconnecting lines shown in the various figures contained herein areintended to represent example functional relationships and/or physicalor virtual couplings between the various elements. It should be notedthat many alternative or additional functional relationships or physicalor virtual connections may be present in a practical electronic datacommunications system.

As will be appreciated by one of ordinary skill in the art, the presentdisclosure may be embodied as a method, a data processing system, adevice for data processing, and/or a computer program product.Accordingly, the present disclosure may take the form of an entirelysoftware embodiment, an entirely hardware embodiment, or an embodimentcombining aspects of both software and hardware. Furthermore, thepresent disclosure may take the form of a computer program product on acomputer-readable storage medium having computer-readable program codemeans embodied in the storage medium. Any suitable computer-readablestorage medium may be utilized, including hard disks, CD-ROM, DVD-ROM,optical storage devices, magnetic storage devices, semiconductor storagedevices (e.g., USB thumb drives) and/or the like.

In the discussion contained herein, the terms “user interface element”and/or “button” are understood to be non-limiting, and include otheruser interface elements such as, without limitation, a hyperlink,clickable image, and the like.

The present disclosure is described below with reference to blockdiagrams and flowchart illustrations of methods, apparatus (e.g.,systems), and computer program products according to various aspects ofthe disclosure. It will be understood that each functional block of theblock diagrams and the flowchart illustrations, and combinations offunctional blocks in the block diagrams and flowchart illustrations,respectively, can be implemented by computer program instructions. Thesecomputer program instructions may be loaded onto a general-purposecomputer, special purpose computer, mobile device or other programmabledata processing apparatus to produce a machine, such that theinstructions that execute on the computer or other programmable dataprocessing apparatus create means for implementing the functionsspecified in the flowchart block or blocks.

These computer program instructions may also be stored in acomputer-readable memory that can direct a computer or otherprogrammable data processing apparatus to function in a particularmanner, such that the instructions stored in the computer-readablememory produce an article of manufacture including instruction meansthat implement the function specified in the flowchart block or blocks.The computer program instructions may also be loaded onto a computer orother programmable data processing apparatus to cause a series ofoperational steps to be performed on the computer or other programmableapparatus to produce a computer-implemented process such that theinstructions that execute on the computer or other programmableapparatus provide steps for implementing the functions specified in theflowchart block or blocks.

Accordingly, functional blocks of the block diagrams and flowchartillustrations support combinations of means for performing the specifiedfunctions, combinations of steps for performing the specified functions,and program instruction means for performing the specified functions. Itwill also be understood that each functional block of the block diagramsand flowchart illustrations, and combinations of functional blocks inthe block diagrams and flowchart illustrations, can be implemented byeither special purpose hardware-based computer systems that perform thespecified functions or steps, or suitable combinations of specialpurpose hardware and computer instructions.

One skilled in the art will also appreciate that, for security reasons,any databases, systems, or components of the present disclosure mayconsist of any combination of databases or components at a singlelocation or at multiple locations, wherein each database or systemincludes any of various suitable security features, such as firewalls,access codes, encryption, de-encryption, compression, decompression,and/or the like.

The scope of the disclosure should be determined by the appended claimsand their legal equivalents, rather than by the examples given herein.For example, the steps recited in any method claims may be executed inany order and are not limited to the order presented in the claims.Moreover, no element is essential to the practice of the disclosureunless specifically described herein as “critical” or “essential.”

With reference to FIG. 1, an analytical recognition system includingvideo observation, surveillance and verification according to anembodiment of this disclosure is shown as 100. System 100 is a networkvideo recorder that includes the ability to record video from one ormore cameras 110 (e.g., analog and/or IP camera). Video cameras 110connect to a computer 120 across a connection 130. Connection 130 may bean analog connection that provides video to the computer 120, a digitalconnection that provides a network connection between the video camera110 and the computer 120, or the connection 130 may include an analogconnection and a digital connection.

Each video camera 110 connects to the computer 120 and a user interface122 to provide a user connection to the computer 120. The one or morevideo cameras 110 may each connect via individual connections and mayconnect through a common network connection, or through any combinationthereof.

System 100 includes at least one video analytics module 140. A videoanalytics module 140 may reside in the computer 120 and/or one or moreof the video cameras 110. Video analytics module 140 performs videoprocessing of the video. In particular, video analytics module 140performs one or more algorithms to generate non-video data from video.Non-video data includes non-video frame data that describes content ofindividual frames such as, for example, objects identified in a frame,one or more properties of objects identified in a frame and one or moreproperties related to a pre-defined portions of a frame. Non-video datamay also include non-video temporal data that describes temporal contentbetween two or more frames. Non-video temporal data may be generatedfrom video and/or the non-video frame data. Non-video temporal dataincludes temporal data such as temporal properties of an objectidentified in two or more frames and a temporal property of one or morepre-defined portions of two or more frames. Non-video frame data mayinclude a count of objects identified (e.g., objects may include peopleand/or any portion thereof, inanimate objects, animals, vehicles or auser defined and/or developed object) and one or more object properties(e.g., position of an object, position of any portion of an object,dimensional properties of an object, dimensional properties of portionsand/or identified features of an object) and relationship properties(e.g., a first object position with respect to a second object), or anyother object that may be identified in a frame. Objects may beidentified as objects that appear in video or objects that have beenremoved from video. Objects may be identified as virtual objects that donot actually appear in video but which may be added for investigativepurposes, training purposes, or other purposes.

Video analytics module 140 may be positioned in camera 110 to convertvideo-to-video data and non-video data and the camera 110 and to providethe video data and the non-video data to the computer 120 over anetwork. As such, the system 100 distributes the video processing to theedge of the network thereby minimizing the amount of processing requiredto be performed by the computer 120.

Computer 120 includes computer-readable medium comprising software formonitoring user behavior, which software, when executed by a computer120, causes the computer 120 to perform operations. User interface 122provides an interface to the computer 120. User interface 122 mayconnect directly to the computer 120 or connect indirectly to thecomputer 120 through a user network.

A user behavior is defined by an action, an inaction, a movement, aplurality of event occurrences, a temporal event, an externallygenerated event, or any combination thereof. A particular user behavioris defined and provided to the computer 120.

An action may include picking up an object wherein the object has beenplaced or left at a particular location. An action may include moving aparticular object such as the opening of a door, drawer or compartment.An action may include positioning (or repositioning) a body part such asplacing a hand in a pocket or patting oneself repeatedly at a particularlocation (an indication that a weapon may be concealed). The action mayinclude moving to a particular position, a first individual engaging asecond individual and/or moving a hand, arm, leg and/or foot in aparticular motion. An action may also include positioning a head in aparticular direction, such as, for example, looking directly at securitypersonnel or a security camera 110. Various other examples have beendiscussed hereinabove.

Inaction may include failing to reach for an object wherein an object isdropped or positioned and the individual (e.g., object) does notretrieve the dropped object. Inaction may also include failing to walkto a particular location or failure to perform a particular task. Forexample, confirming that a security door is locked would require theaction of approaching the door and the action of striking the door toensure that it would not open. As such, the user behavior may be definedas the inaction of approaching the door and/or the inaction of strikingthe door to confirm that the door will not open. Various other examplesof inaction have been discussed hereinabove.

A temporal event may include the identification of a customer thatabruptly leaves a store, an individual dwelling at a store entrance orexit, an individual remaining in a particular location for a time periodexceeding a threshold. Various other examples of a temporal event havebeen discussed hereinabove.

A user may identify a particular user behavior and provide and/or definecharacteristics of the particular user behavior in the computer 120.Computer 120 receives non-video data from the camera 110 wherein thenon-video data includes behavioral information data. The particular userbehavior may be defined by a model 143 of the behavior where the model143 includes one or more attribute such a size, shape, length, width,aspect ratio or any other suitable identifying or identifiable attribute(e.g., tattoo or other various examples discussed herein). The computer120 includes a matching algorithm or matching module 141, such as acomparator, that compares the defined characteristics and/or the model143 of the particular user behavior with user behavior in the definednon-video data. Indication of a match by the matching algorithm ormodule 141 generates an investigation wherein the investigation includesthe video data and/or non-video data identified by the matchingalgorithm 141. Investigations are a collection of data related to anidentified event, and generally document behaviors of interest. As such,investigations require further review and investigation to understandthe particular behavior.

The investigation may be sent to other cameras or systems on a givennetwork or provided over a community of networks to scan for a match oridentify and alert. Matching algorithm 141 may be configured as anindependent module or incorporated into the video analytics module 140in the computer 120 or in any cameras 110. The video analytics module140 may also include a comparator module 142 configured to compare themodel 143 of the particular user behavior and the non-video data.

A particular user behavior may be defined as positioning a head towardan observation camera 110 exceeds a preset period or positioning of ahead directly toward a manager's office exceeds a preset period. Thisparticular user behavior is indicative of a customer trying to identifythe observation cameras 110 in a store in an effort to prevent beingdetected during a theft or an employee trying to determine if a manageris observing his/her behavior. The video analytics module 140 performsan algorithm to generate non-video data that identifies the headposition of objects. The video analytic module 140 may also provide avector indicating the facial and/or eye direction. The matchingalgorithm 141 searches the non-video data to determine if the headposition and/or vector indicating facial direction exceeds the presetperiod. A match results in the generation of an investigation.

With reference to FIG. 2, a video/image sequencer according to anembodiment of this disclosure is shown as 200. Sequencer 200 isconfigured to receive video, video data, non-video data, video sequencesand/or still images from various sources of video. For example,continuous video may be provided from locations 1 and 2, while motiononly data may be provided from location 7. Video clips of short durationmay be provided from locations 3 and 6 and still images may be providedfrom locations 4 and 5. This data may be communicated to the sequencer200 by any suitable communications medium (e.g., LAN, WAN, Intranet,Internet, hardwire, modem connection, wireless, etc.).

Sequencer 200 generates a time-stamp from data provided with the videoand/or image data. The time-stamp may be embedded into the video and/orimage data, provided as part of the video and/or image, or a time-stampmay be provided with the file containing the video and/or image data.Alternatively, sequencer 200 may be configured to receive user-entereddata, included time-stamp information, associated with each input.

Sequencer 200 may additionally, or alternatively, generate ageo-location from the data provided with the video and/or image date.Geo-location information may be embedded into the video and/or imagedata, provided as part of the video and/or image, or provided with thefile containing the video and/or image data. For example, video and/orimage may contain a land-marking feature that may be used to identifythe location where the picture was taken.

Sequencer 200 may additionally, or alternatively, generate field-of-viewdata (hereinafter “FOV data”) for video and/or image data. FOV data maybe obtained from the camera location information, obtained from theinformation contained within the video (e.g., landmark identification)and/or entered by a user.

FIG. 4 is an illustration of an image map 300 and an associated timeline310 generated by the sequencer 200. Sequencer 200 may be configured toutilize the time-stamp data, geo-location data and/or FOV data toassemble an image map 300 and timeline 310 from all video and image data(or any portions thereof) provided to the sequencer 200.

A user may provide the sequencer 200 with a particular time and/ortimeframe and the sequencer provides all video and/or images related tothat particular time. Time and/or timeframe may be selected on thetimeline 310 and the image map 300 may be updated to include all videoand/or image data related to the selected time and/or timeframe.

A user may additionally, or alternatively, provide the sequencer 200with a selected location and the sequencer provides all video and/orimage data related to that particular location. Selected locations maybe selected on the image map 300 or provided as geo-location data to thesequencer 200.

A user may additionally, or alternatively, provide the sequencer 200with a particular time and/or timeframe in addition to a geo-location tofurther narrow and isolate all video and/or image data related to thatparticular location.

After a particular time, timeframe and/or geo-location is used toidentify video and/or image data, the user may utilize the searchingalgorithms, methods and system described herein to identify particularitems of interest, patterns and/or individuals contained within thevideo and/or image data.

It is important to note that the present disclosure goes beyond facialrecognition software (which may be utilized in conjunction herewith) andprovides additional algorithms and analytics for tracking and/orinvestigative purposes as explained below. In addition, it is notnecessary in certain instances that facial recognition be utilized toflag or track someone or something and the presently-described systemmay be employed without facial recognition software or algorithms whichmay prove insensitive to certain moral, federal or local laws.

The present disclosure also relates to an analytical recognition systemfor real time/post time object tracking based on pre-programmedparameters, e.g., real time and post time analysis, recognition,tracking of various pre-programmed (or post programmed) known objects ormanually programmed objects based on shape, color, size, number ofcertain objects on a person(s), oddity for a particular circumstance(e.g., winter coat in 80° heat), similarity of particular object overthe course of a particular time frame (similar items, e.g., backpacks,within particular area), separation of a sensitive object(s) from personfor a preset period of time, odd object in particular area, objectsplaced near sensitive objects, similar objects being placed in similarareas and separated from person, particular color contrasts andcombinations (e.g., red shirt exposed under black shirt, or white hat onblack hair).

Programmed objects may include objects with a particular known shape,size color or weight (as determined by number of people carrying, gaitof person carrying, how the object is being carried, etc.) or based upona look up library of objects and mapping algorithm. These objects may bepre-programmed into the analytical software and tracked in real timeand/or post time for analysis. Manually programmed objects may beinputted into the software by color, size, shape, weight, etc. andanalyzed and tracked in real time and/or post time to determine abnormalconditions or for other purposes. Manually programmed objects may beuploaded for analysis in real time, e.g., facial recognition images,tattoos, piercings, logos, or other indicia as explained in more detailbelow. Additionally, a user generated item and/or image may be generatedfrom video data (e.g., frame data) and/or a still image and provided foranalytics. For example and as shown in the an analytical recognitionsystem 500 of FIG. 4, an object 510 (e.g., hat, backpack, outfit, or anyidentifiable feature) identified in a still image and/or a video frame(or identified as a result of one of the abnormal conditions describedherein) may be isolated from an individual 505 for a preset amount oftime (temporal event) and provided as a user generated item 510′ foridentification in live-video 520 or searched and identified in storedvideo 525, e.g., video frames and/or still images.

System 500 may include video analytics module 140 that is configured toperform real time and/or post time analysis of video and tracking ofevery person with a backpack 510 within a particular area or within aparticular camera view 505. Suspicious behavior and/or behavior ofinterest of one or more persons may be tracked and recorded and analyzedin either real time or post time. For example as identified in FIG. 5,if the backpack 510 is separated from a person 505 and left for apredetermined period of time, this video may be flagged for real timealerts and/or post time analysis. The object, e.g., backpack 510, mightbe flagged, time stamped and/or separated into an individual videostream for analysis later. A user in real time or post time analysis canzoom in for high-definition tracking or for incorporation into avideo/image sequencer 200 as discussed herein. The person 505 dropping apreprogrammed suspicious object, e.g., backpack 510 (or any other objectthat is recognized by a library of images 530, user generatedimage/object 535 (via an input device) or a certain mapping algorithm ormodule 140) may be tracked and analyzed for real time alerts and/or posttime analysis. The system 500 may both track the object 510 and flag andtrack the person 505 for real time or post time analysis through one ormore cameras 110 or a network of cameras 110, 110 a, 110 b, etc.

In other example, the system 500 may flag and track in real time foralert purposes or post time analysis a person wearing a winter coat inthe Summer, a long raincoat when sunny, etc. This would also beclassified as an alert or abnormal condition.

The system 500 may be capable of combining pre-programmed analytics toalert for one or more (or a combination of) abnormal scenarios. Forexample, a person carrying a case capable of carrying an semi-automaticor automatic rifle and that person loitering outside of a sensitivebuilding for a pre-determined period of time may be automaticallyflagged, tracked and an alert sent to security.

The system 500 may be capable of tracking and analyzing particularobjects and the software or video analytics module 140 may bepre-programmed to identify the same objects in later obtained videostreams and/or still images. For example, a person of particularimportance is scheduled to have a press briefing or scheduled to arriveat a particular location at a specific time. The scheduled event ispostponed (intentionally or unintentionally). The software or videoanalytics module 140 may be preprogrammed to recognize certain objects(or persons with objects 510 or user generated objects 535) appearing innewly generated video for the re-scheduled event. In certain instances,the original video from the original time of the scheduled event may bereviewed and a user may pre-program the software or video analyticsmodule 140 to look for certain “repeat” objects 510 (backpacks, coats,hats, clothing, briefcases, persons, etc.) in the real time videofootage of the now re-scheduled event. A person may also be classifiedas a loiterer and flagged for review at the later scheduled event. Awarning can be sent to the security team reviewing the tapes in realtime if that was a person of interest.

The video analytics module 140 may be configured to recognize abnormalpatterns of behavior or unexpected patterns of behavior and alertsecurity or investigators of potentially abnormal scenarios, events orconditions. The video may be configured for real-time analytics or postevent analysis. For example, the video analytics module 140 can beprogrammed to recognize convergence patterns toward a particulargeographical area and/or divergence patterns away from a particulargeographical area. Global positioning software and vectoring may beutilized to accomplish this purpose. Recognition of convergent patternsand/or divergent patterns may be helpful in automatically recognizingpotential flash mobs, mass robberies or other abnormal events. Forexample and as shown in FIG. 5, analytical recognition system 600includes video analytics module 140 which may be configured to track anabnormal number of patrons 604 a-604 l arriving at a particular location620 at or near a particular time 622. The video analytics module 140 mayalso be configured to tract abnormal velocity of patrons 604 a-604 land/or individuals arriving or departing from a particular location 620.A typical arrival and/or departure velocity may be preset or obtainedfrom an algorithm of previous individuals that may have arrived ordeparted from a particular location over a preset or variable amount oftime. Deviation from the arrival and/or departure velocity may triggeran abnormal condition.

A security system 600 with the video analytics module 140 and one ormore camera arrays or systems 610 a-610 g may be configured to recognizean abnormal number of people converging towards a particulargeographical area 620 over a preset time. The video analytics module 140may be configured to utilize vector analysis and/or image and datavector analysis algorithms and/or machine learning algorithms to assessone or more convergence patterns. Moreover, the system 600 may beconfigured to recognize similarities in clothing, age, articles beingcarried (e.g., briefcases, backpacks, other similar items) and alertsecurity or investigators of a possible abnormal condition. This can beuseful in recognizing so-called “flash mobs” or other highly sensitivesituations during a parade, marathon, political speech, etc.

Divergence patterns and/or velocities may be used to identify unusualpatterns of individuals departing from a particular area 620. Forexample, in the event of a panic-like situation the divergence velocityof individuals is expected to be greater than a preset or calculatedaverage divergence velocity. As such, identification of one or moreindividuals leaving the particular area and/or situation at a velocityless than the average velocity or the panic-like velocity may indicatethat the individual was not in a panic-like condition possibly due tothe fact that he/she perpetrated or were aware of the particularpanic-like situation. Moreover a person leaving an area with a higherthan average velocity may be “running from an event”, e.g., running froma robbery or away from an upcoming explosion.

The video analytics module 140 may also be configured to monitor webtraffic and/or social media sites (Facebook®, Myspace®, LinkedIN®)relating to a particular location and/or event and provide alerts ofthat nature to security or combine web traffic relating to an event orgeographic area with video analytics that recognize convergence patternsto alert of a potential flash mob or gang robbery. The video analyticsmodule 140 may also work in reverse and access web traffic or varioussocial media sites when a convergence pattern is recognized and ping oneor more of these sites to gather additional information to possiblyuncover more pattern activity or uncover a flash mob event at aparticular location.

The video analytics module 140 may also be configured to monitor webtraffic or social media sites for activities that precede a particulartime stamp. For example, a social media posting conveys condolences fora particular event that coincides or precedes the particular event mayindicate foreshadowing of the event and indicate prior knowledge of theupcoming event.

The system 600 and video analytics module 140 may be configured toanalyze video from one or more street cameras, parking lot cameras,store/mall camera, or other camera systems 610 a-610 g to determinepre-programmed abnormal conditions or manually programmed conditions inreal time. The system 600 may be configured to provide an alert if anabnormal number of cars are converging at a particular spot (e.g.,shopping mall), and couple that information with footage from theparking lot surveillance cameras to ascertain how many people areconverging on a particular store or place and couple that analytic withthe in-store camera to determine loitering at a particular spot at aparticular time or delta time. This is typical behavior of a flash mobor gang robbery. Again, the system 600 might tie into one or more socialmedia sites for additional information and/or confirmation.

Similarly, the velocity patterns of the approaching cars, obtained fromvideo, and/or the velocity at which individuals depart from their carsmay also be indicative of abnormal condition.

Other examples of analytics that the video analytics module 140 mayperform in real time and/or post time may relate to gang-typerecognition. For example, the analytical recognition system 700 of FIG.6 may be configured to recognize gang colors and/or color combinationsand/or patterns and flag the video 718 and/or alert security if anabnormal number of individuals (or abnormal % of individuals) withparticular colors or color combinations and/or patterns are convergingon, or loitering in, a particular geographical area. The video analyticsmodule 140 may be pre-programmed to recognize a particularcharacteristic or trait 715 of an individual or individuals 705 a, e.g.,clothing, head gear, pant style, shirt/coat colors, the manner it isworn, symbols, coat logos, tattoos, piercings, hair style, handgestures, cars, motorbikes, etc. and alert security of an abnormalcondition or a previous investigation stored as a previous image 725 ina computer 720. These individuals 705 a may be flagged and tracked for apreset period of time or until he/she leaves the area. The overall imageand characteristics 715 of a particular group of patrons in a crowd(similarities of colors, uniform, gear, clothing style, hair style,logos, piercings, tattoos, symbols, other gang-related indicia, cars,motorbikes or clothing, etc.) may be recognized and trigger an alert.The video analytics module 140 may provide an alert that x % ofindividuals in a particular crowd have a particular trait 715, e.g.,same tattoo, red shirts on, have the same logo, hair style are carryinga specific object, etc. The video analytics module 140 may be configuredto provide an alert based on an assessment that a predetermined numberof individuals in a particular crowd have a particular trait 715.

The video analytics module 140 may be configured to provide graphicalrepresentations of numerous abnormal conditions to better facilitaterecognition of patterns or very high levels (and/or predeterminedlevels) of one or more abnormal conditions. This may allow a highernumber of patterns to be tracked and analyzed by one or moreindividuals. The video analytics module 140 may also recognize contactbetween individuals wherein the contact may be physical contact (e.g.,handshake, an embrace or exchange of an object) or contact may benon-contact (e.g., engage in conversation, prolonged eye-contact orengaging in other non-physical contact that would indicateacknowledgement therebetween).

Other alert-type conditions may relate to abnormal scenarios wherein thevideo analytics module 140 recognizes an object being carried by anindividual 705 b that is unusual for a particular area. For example asshown in FIG. 6, a person carrying a pitchfork or shovel (not shown) ina mall 723, or a group (705 b and 705 c) carrying bats 716 in mall 723and converging on a particular area. Again, real-time analysis of thevideo would be most useful and provide security with an abnormalcondition alert. Post analysis may be helpful for determining offendersshould an event take place when authorities are called to assist.

With any of the aforedescribed scenarios or alerts noted herein, thevideo analytics module 140 may work in conjunction with a video libraryof images or algorithms 750 to trigger alerts or respond to queries.Additional images, such as a library images and/or user-generated images750, may be provided as inputs to the video analytics module 140 andused to analyze video through the recognition aspects of the videoanalytics module 140. This may all happen in real time or during posttime analysis. Again, queries may be entered depending upon a particularpurpose and the system 100, 400, 500, 600, 700 and/or 800 can in realtime or post time analyze video for the queried conditions.

The system 100, 400, 500, 600, 700 and/or 800 may be configured toperform three-dimensional face recognition. The system 100, 400, 500,600, 700 and/or 800 may be manually programmed to recognize anindividual or suspect 705 a in an investigation (or prior felon) basedon clothing type, piercings, tattoos, hair style, etc. (other thanfacial recognition which may also be utilized depending on authority ofthe organization (FBI versus local mall security)). An image of asuspect 705 a may be scanned into the video analytics module 140 anditems such as piercings, tattoos, hairstyle, logos, and headgear may beflagged and uploaded into the image database for analyzing later in realtime or post time analysis. For example, if a thief 705 a robs aconvenient store and his/her facial image is captured onto one or morecameras 710, not only may his/her image be uploaded to all the storecameras 710, but other identifying information or characteristics ortraits 715 as well, e.g., hair style, tattoos, piercings, jewelry,clothing logos, etc. If the thief 705 a enters the store again, an alertwill automatically be sent to security. Even if the system recognizes asimilar tattoo or piercing pattern or logo 715 on a different personthat person may be deemed a suspect for questioning by authorities.Again, this goes beyond mere facial recognition wherein that so-calleddifferent person would not necessarily be flagged and tracked.

The system 100, 400, 500, 600, 700 and/or 800 may also generate alibrary of individuals and/or patrons that regularly frequent or visit aparticular location thereby eliminating the need to track theseparticular individuals and allowing the system 100, 400, 500, 600 or 700to focus on identification and tracking of individuals not previouslyidentified and saved in the library. The library of patrons (not shown)may also link to a Point-of-Sale (POS) system thereby validating thatthe individuals identified and stored in the library are regularpatrons.

As best shown in FIG. 7, another analytical recognition system 800 isshown with the video analytics module 140 being utilized with a chain ofstores, a mall or a series of stores 850 in a town or community. Thecommunity of stores or a chain of stores 850 a-850 e is able to sharevideo images 824 and other identifying information of characteristic ortrait of known felons 805 across a network of cameras 810 a-810 eutilizing the same the video analytics module 140 (or uploading theimage 824 and identifying information on an individual store analyticalsystem 840 a-840 e). These local storeowners or store chains 850 a-850 emay be able to prevent additional losses by flagging and tracking knownindividuals 805 of particular interest (based on a prior characteristicsor traits as described above and/or identifying information entered intoan image and/or information database) once he/she 805 enters a store,e.g., store 850 a. Alerts may be sent to local authorities of theseindividuals (or group of individuals) and they may be tracked throughoutan entire network of cameras 810 a-810 e, including parking lot cameras,street cameras, etc. along a community network. Once an individual 805is flagged and there is an alert, other information may be capturedrelating to car, car type, car route, accomplices, etc. Further, allcameras 810 a-810 e in the system 800 may be alerted to flag and trackthe individual 805 and accomplice in real time and/or for post timeanalysis.

The various described systems 100, 400, 500, 600, 700 and 800 may alsobe utilized to identify individuals with a “no contact” condition. Forexample, a building resident may have a restraining order issued by acourt that prevents a particular individual from being within a certainproximity. The image, e.g., 824, may be entered into the system e.g.,system 800 and the video analytics module 140 may identify theindividual 805 and provide notice and/or documentation to the buildingresident and/or the authorities. Similarly, a government-generateddatabase 820 may be provided to the system 800 wherein the database 820includes a library of images 824 of individuals 805 identified in aparticular legally mandated registration program.

A community may choose to set up a community network of cameras 810a-810 e for this purpose. New owners of local businesses may opt toupload a particular felon's image 824 for analyzing (i.e., for localalerts) on a per occurrence subscription (e.g., dollar amount), e.g., aparticularly violent felon's image 824 and additional identifyinginformation may be of particular interest to an entire community foruploading on all networked cameras 810 a-810 e (or even stand alonesystems) while a small time shoplifter may not be of interest.

The video analytics module 140 may also utilize gait as an indicator ofan individual or suspect, limp, shuffle, head angle, stride, hand sway,hand gestures, etc. A person's gait is as individual as a fingerprintand may be used to identify disguised felons. Many variables contributeto an individual gait and this information can be uploaded to the videoanalytics module 140 (e.g., walk velocity, step frequency, angle betweenfeet, hand/arm position, hand/arm sway, limp, shuffle, etc.)

The video analytics module 140 may also be configured to alert securityif a certain number of known images or events or habits occurs within aparticular time period (e.g., self patting of particular area(s) Xnumber of times within preset time period, patting or clenching of aknown area for carrying or hiding weapons, nervous twitching or rapidhead turning X number of times, leering around corners, looking at videocameras X number of times within a preset time period, etc. The videoanalytics module 140 may be configured to alert security or provideinformation to a user based on an abnormal or excessive habit or eventoccurring within a preset time limit or a combination of any of theevents occurring within a preset time period. For example, a personwalking through a store with hand clenched atop pants with rapid headturning may trigger an alert or abnormal situation. In another example,security is flagged or highlighted (or otherwise identified in a certainarea(s) by the system 100, 400, 500, 600, 700 and/or 800) and a suspectleering in that direction repeatedly or repeatedly turning his/her headin that direction may trigger an alert or abnormal situation. In anotherexample, an individual shopping and/or lingering in an area of a storethat is typically an area with short dwell times (e.g., dwell time for amale in the make-up area is typically short while dwell-time for afemale is typically, if not always, long).

As mentioned above, the analytical recognition system 100, 400, 500,600, 700 and/or 800 of the present disclosure may be utilized todetermine gun or weapon detection by virtue of pre-programming certainhabitual behavior into the video analytics module 140 and analyzing thesame (in real time and/or post time). For example, a person repeatedlygrabbing a certain area known to house weapons and walking with acertain gait (e.g., walking with a limp might indicate carrying ashotgun) may be an indication of the person carrying a weapon. Thisinformation may be analyzed with other identifying information orindicia (e.g., tattoo, gang color, gang symbol, logo, etc.) to triggeran alert or abnormal situation. In another example, an individual iswearing a trench coat when it is not raining or on a sunny day in theSummer and leering or head turning. In this instance, the videoanalytics module 140 would need some sort of sensory input regardingrain or temperature or sunshine (light) and/or a connection to a systemthat provides such data. The time of day might also become a trigger oradditional event that is preprogrammed into the video analytics module140 analytics to heighten “awareness” of the video analytics module 140when triggering alerts, e.g., very late at night or past midnight whenmore robberies tend to occur.

In other examples, the video analytics module 140 may allow the securitypersonal to query the analytical recognition system 100, 400, 500, 600,700 and/or 800 in real time or post time: “How many people with redbaseball caps have entered the store or area within the delta of 5-10minutes?”; “How many people are converging on the central fountain atthis time or over this delta time?”; “How many people have lingered atthe fountain for delta minutes?” Other queries may include instructions:“Scan and recognize/flag/follow/track people wearing long pants orwinter coats (when 90° degree Summer day)”; “Scan andrecognize/flag/follow/track people wearing red hats”; “Scan andrecognize/flag/follow/track people carrying multiple backpacks”; “Scanand recognize/flag/follow/track people who have left objects (e.g.,backpacks unattended)—track person over system, multiple systems, flaglocation of object, etc.”; “Scan and recognize/flag/follow/track peopleloitering near sensitive areas, leaving objects near sensitiveareas—track person over system, multiple systems, flag location; and/or“Alert if a delta number of unattended objects left at preset time orover preset time”.

In another example, the video analytics module 140 may be configured toperform real-time video processing and analyzation to determine a crowdparameter (e.g., a real-time crowd count or a real-time crowd densityestimation) by automated processing of the video sequence of a physicalspace. The video analytic module 140 may include one or more algorithmsconfigured to determine a rate of change in the crowd parameter. Therate of change in the crowd parameter may be indicative of crowdconvergence or crowd divergence.

When the rate of change in the crowd parameter exceeds a predeterminedthreshold, the video analytics module 140 automatically issues an alert.For example, when the rate of change in the crowd parameter isindicative of crowd convergence, the video analytics module 140 mayalert security of a potential flash mob or gang robbery. The videoanalytics module 140 may be configured to utilize vector analysis and/orimage and data vector analysis algorithms and/or machine learningalgorithms to assess one or more convergence patterns.

The video analytics module 140 may be connected to an array of cameras610 a-610 g organized in a network, and upon issuance of an alert eachcamera in the network may be utilized to track one or more objects orindividuals (e.g., patrons 604 a-604 l shown in FIG. 6). When the rateof change in the crowd parameter is indicative of crowd divergence, thevideo analytics module 140 may alert security of a potentially hazardoussituation or criminal activity.

As various changes could be made in the above constructions withoutdeparting from the scope of the disclosure, it is intended that allmatter contained in the above description shall be interpreted asillustrative and not in a limiting sense. It will be seen that severalobjects of the disclosure are achieved and other advantageous resultsattained, as defined by the scope of the following claims.

What is claimed is:
 1. An analytical recognition system, comprising: atleast one video camera configured to capture video; and a videoanalytics module configured to: perform real-time video processing andanalysis of the captured video, generate non-video data, implement oneor more algorithms to identify an abnormal situation, each abnormalsituation alerting the video analytics module to automatically issue analert and track one or more objects or individuals by utilizing the atleast one camera, wherein the abnormal situation is selected from thegroup consisting of action of a particular individual, inaction of aparticular individual, a temporal event, and an externally generatedevent, generate a library of individuals who visit a location with apredetermined degree of regularity, and exclude, from being tracked,individuals who are listed within the library of individuals and whohave been determined to have visited the location with the predetermineddegree of regularity.
 2. An analytical system according to claim 1,wherein the video analytics module identifies and stores in a databaseone or more characteristics of the particular individual for futurerecognition by the video analytics module and instructions forimplementing the one or more algorithms to identify an abnormalsituation.
 3. An analytical system according to claim 2, wherein the oneor more characteristics of the particular individual is selected fromthe group consisting of hair style, tattoos, piercings, clothing, logos,contrasting colors, gang-related indicia, and jewelry.
 4. An analyticalsystem according to claim 2, wherein the one or more characteristics ofthe particular individual includes a person's gait.
 5. An analyticalsystem according to claim 4, wherein each person's gait is determinedbased on a combination of one or more of: limp, shuffle, head angle,stride, hand sway, arm sway, hand gestures, walk velocity, stepfrequency, angle between feet, hand position, and arm position.
 6. Ananalytical system according to claim 1, wherein the video analyticsmodule stores the captured video in a database accessible by a user andwherein the user identifies one or more characteristics of theparticular individual for future recognition by the video analyticsmodule and the one or more algorithms to identify an abnormal situation.7. An analytical system according to claim 6, wherein the one or morecharacteristics of the particular individual is selected from the groupconsisting of hair style, tattoos, piercings, clothing, logos,contrasting colors, gang-related indicia, and jewelry.
 8. An analyticalsystem according to claim 1, wherein the video analytics module connectsto an array of cameras organized in a network and wherein, upon issuanceof an alert, each camera in the network is utilized to track one or moreobjects or individuals.
 9. An analytical system according to claim 1,wherein the video analytics module identifies and stores in a databaseone or more characteristics of the particular individual for futurerecognition by the video analytics module and instructions forimplementing the one or more algorithms to identify an abnormalsituation and issue an alert, and wherein the video analytics moduleconnects to an array of cameras organized in a network to analyzecaptured video.
 10. An analytical system according to claim 9, whereinthe one or more characteristics of the particular individual is selectedfrom the group consisting of hair style, tattoos, piercings, clothing,logos, contrasting colors, gang-related indicia, and jewelry.
 11. Ananalytical system according to claim 9, wherein an owner of one of thecameras in the array of cameras forming the network may opt on asubscription basis for receiving particular alerts or being part of thecamera network.
 12. The analytical system according to claim 1, whereinthe video analytics module is further configured to link to apoint-of-sale system to validate that the individuals identified andstored in the library of individuals visit the location with apredetermined degree of regularity.